Comparative Study of Differentially Private Data Synthesis Methods

TL;DR

This paper compares various differentially private data synthesis methods, evaluating their utility and privacy guarantees through extensive simulations to guide practical application and future research.

Contribution

It provides a comprehensive comparison and evaluation of current DIPS techniques, highlighting their strengths, limitations, and practical utility.

Findings

DIPS methods vary in utility and privacy trade-offs.

Some techniques maintain statistical properties well.

The study identifies promising directions for future DIPS research.

Abstract

When sharing data among researchers or releasing data for public use, there is a risk of exposing sensitive information of individuals in the data set. Data synthesis (DS) is a statistical disclosure limitation technique for releasing synthetic data sets with pseudo individual records. Traditional DS techniques often rely on strong assumptions of a data intruder's behaviors and background knowledge to assess disclosure risk. Differential privacy (DP) formulates a theoretical approach for a strong and robust privacy guarantee in data release without having to model intruders' behaviors. Efforts have been made aiming to incorporate the DP concept in the DS process. In this paper, we examine current DIfferentially Private Data Synthesis (DIPS) techniques for releasing individual-level surrogate data for the original data, compare the techniques conceptually, and evaluate the statistical utility and inferential properties of the synthetic data via each DIPS technique through extensive simulation studies. Our work sheds light on the practical feasibility and utility of the various DIPS approaches, and suggests future research directions for DIPS.